Hamiltonian Monte Carlo For Dummies (Statisticians / Pharmacometricians / All)

[@prathmeshmaharshi7045]

Amazing work. I think I am in a position now to have some hands on experience. I think covering NUTS first would still be better. Also thanks for the final part where you discussed the complexity and high dimensionality of models. Glad it works pretty well.

[@alanmaloney2791]

Many thanks for the friendly and positive reply. Much appreciated!

[@xondiego]

Jeez after watching this and Ben's Lambert, I am ready to try by my own the HMC, thanks a lot that sucha good presentation on the intuition of the HMC.

[@alanmaloney2791]

Glad you found it useful!

[@weideng7661]

Great blog. The best HMC video I have seen before. Thanks.

[@alanmaloney2791]

Thanks for the friendly feedback...delighted you liked it!

[@keqiaoli4617]

Thanks for the insightful video. Could you please provide some sample codes of the figures you show on the slides? Thank you

[@user-wr4yl7tx3w]

Thank goodness for KZbin recommendation.

[@user-wr4yl7tx3w]

Sorry may be I missed it but did you discuss how it can traverse other contours? Your example showed how it can stay in its contour plane.

[@alanmaloney2791]

Hi Y. Good question. HMC follows the joint contour 'within' an iteration (i.e. for the L=15 steps) ...see the video at around 9:48, where it followed the contour with m=1.00 for iteration 1 to yield the new value for theta (e.g. -0.65 in the video). For the next iteration, it will sample a new m variable (say m=0.00). This will 'shift' us to a new joint contour for iteration 2 (the intersection of theta = -0.65 and m=0.00 on the graph). Thus 'between' iterations is when we "jump" between the joint contours. Hope this is clear.

[@theresau1206]

Thank you for this very illustrative and insightful video.

[@alanmaloney2791]

Great to hear - delighted someone thought it was useful!

[@colorizedenhanced-silentmo5628]

Bonjour, Alan Maloney. this is a colorful video. thanks. :)

[@yli6050]

Thank you for great explanation ❤

[@고라니-k8g]

Thanks for the amazing video. It helped a lot!!

[@dominicj7977]

I have some doubts. What does stan do that other languages like R or python doesn't? Can I use stan for constrained optimisation? I have a problem I need help with. Its about maximising an objective function subject to set of inequality constraints. Would you be abe to help or give some directions?

[@alanmaloney2791]

Hello. R currently doesn't have anything like Stan. I am not sure about Python, but I doubt it. Stan is not built to solve constrained optimisation problems - rather to fit complex Bayesian models. My only advice would be to google for tools/packages to solve constrained optimisation problems like yours. For simple problems, I expect many tools would have something. However for more complex problems, you may need more specialised software. Good luck. Al

[@dominicj7977]

@@alanmaloney2791 My maximisation function is a log likelihood function, similar to how we solve MLE, but subject to some constraints regarding a distribution parameter theta. So not sure if it counts as a "Bayesian".

[@alanmaloney2791]

Hello. If the constraint is on a parameter, Stan can handle that (see their website). If you have many constraints like theta1+theta2+theta3 = 100, theta1*theta3 = 10 etc., I think it may be more tricky to code in Stan. If you are wishing to do MLE, other software may be easier. Cheers Al

[@LingJiong04]

very confusing

[@samoh7101]

Quickly lost.